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Suhaila

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Data Warehouse VS OLAP cube


What is the difference between data warehouse and OLAP cube? Are they deprecated in comparison with each other? Are there any performance issues in one of them?



- Suhaila
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Bjergsen

3

A data warehouse is a federated repository for all the data that an enterprise's various business systems collect. The repository may be physical or logical.

A data warehouse is :

-subject-oriented

-integrated

-timevarying

-non-volatile

Data warehousing emphasizes the capture of data from diverse sources for useful analysis and access, but does not generally start from the point-of-view of the end user who may need access to specialized, sometimes local databases. The latter idea is known as the data mart.

There are two approaches to data warehousing, top down and bottom up. The top down approach spins off data marts for specific groups of users after the complete data warehouse has been created. The bottom up approach builds the data marts first and then combines them into a single, all-encompassing data warehouse.

How is a data warehouse different from a regular database?

Data warehouses use a different design from standard operational databases. The latter are optimized to maintain strict accuracy of data in the moment by rapidly updating real-time data. Data warehouses, by contrast, are designed to give a long-range view of data over time. They trade off transaction volume and instead specialize in data aggregation.

A cube can be considered a multi-dimensional generalization of a two- or three-dimensional spreadsheet. For example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses. Product, time, city and scenario (actual and budget) are the data's dimensions.[3]

Cube is a shortcut for multidimensional dataset, given that data can have an arbitrary number of dimensions. The term hypercube is sometimes used, especially for data with more than three dimensions. A cube is not a "cube" in the strict mathematical sense, as all the sides are not necessarily equal. But this term is used widely.

Slicer is a term for a dimension which is held constant for all cells so that multi-dimensional information can be shown in a two-dimensional physical space of a spreadsheet or pivot table.

Each cell of the cube holds a number that represents some measure of the business, such as sales, profits, expenses, budget and forecast.

OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Although it stores data like a traditional database does, an OLAP cube is structured very differently. Databases, historically, are designed according to the requirements of the IT systems that use them. OLAP cubes, however, are used by business users for advanced analytics. Thus, OLAP cubes are designed using business logic and understanding. They are optimized for analytical purposes, so that they can report on millions of records at a time. Business users can query OLAP cubes using plain English.

I don't think you can really compare them ..



- Bjergsen
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Lili96

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A data warehouse is a database with a design that makes analyzing data easier (often with data from multiple sources). It is usually composed of fact tables and dimension tables, and often aggregate tables.

OLAP is a set of operations that one can do on a data set, such as pivoting, slicing, dicing, drilling. For example, one can do OLAP operations with Excel PivotTables.

You can have a data warehouse and not use OLAP at all (you just run reports).

You can also do OLAP operations on something other than a data warehouse, such as a flat file.

An OLAP Server is a type of server software that facilitates OLAP operations, for example with caching and query re-writing. OLAP operations are often expressed in MDX, and your OLAP server might translate MDX into regular SQL for your database. Or it might work against its own binary file format.

Are one of them deprecated in comparison with other?

No, they compliment each other in that a data warehouse makes it easy to analyze data using OLAP, and OLAP can make analyzing a data warehouse more useful.

Is there any performance issues in one of them?

Yes. A data warehouse is meant to store lots and lots of data, and thus it will take time to query. Performance can be improved by using indexes, caching, and by pre-aggregating some data.

Reference:http://stackoverflow.com/questions/18916682/data-warehouse-vs-olap-cube



- Lili96
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JiaQii

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A data warehouse is simply a database that houses information to support decision-making, managed separately from a company’s operational database. It supports the processing of organizational information by offering a stable platform of consolidated, transactional, organized data. On the other hand, OLAP stands for online analytical processing and cube is another word for a multi-dimensional set of data, so an OLAP cube is a staging space for analysis of information. Basically, a cube is a mechanism used to query data in organized, dimensional structures for analysis. These two options have different IT requirements.

Data Warehouses historically have been a development project that can be pretty pricey just to build. However, data warehouses are now also being offered as a product – fully built, configurable, and able to house multiple types of data. Some data warehouse solutions can be managed by the business user. An OLAP cube is not an open SQL server data warehouse, so it requires someone with OLAP technical skills and experience to manage the server. This translates to specific personnel requirements, but as OLAP cubes are being used all over the business sector, there is logically enough people in the workforce with the skill set required to manage the cube – if the budget allows for the expense of this position. While cost is an important factor to consider, there are several characteristics to think about.

Understanding data warehouses and OLAP cubes better is extremely beneficial for making choices about implementation of BI tools. For some, since company data is needed for such regular analysis, data storage could very well drive the route an organization would take for acquiring BI solutions. For others, the investment in a vault of transactional data might be a secondary purchase, in response to BI processing needs. Either way, the differences are important when making a data storage decision.


A Data Warehouse is organized with business user accessibility at the center of design. It is subject-structured, meaning that it is organized around topics like product, sales, and customer. Because data must be replicated from an Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or any other data organization system, data warehouses are not used for live analysis. Instead, the information housed within data warehouses can be used for periodical reporting; planning, forecasting, and modeling; and in dashboards or graphical scorecards where trends and trajectories of company data can be visually analyzed. Moreover, a warehouse can house a wide variety of data types.

A data warehouse is crafted in such a way that it can integrate several, disparate data sources to create a consolidated database. This is done through data cleaning and data integration techniques that are “smart” processes innate to the data warehouse. Therefore, a company can store personnel data, financial transactions, and any other organizational information all in one place – going beyond numbers and cash flow. It is a very accessible storage unit where data is replicated and transformed from the operational environment. There are really only two operations when accessing the data: the initial loading of the information and the access itself. Data warehouses do not require any recovery, transaction processing or concurrency control mechanisms – they stand simply and securely on their own. Data warehouses are so efficient in terms of business user accessibility because of their SQL server framework. However, they can be bought directly already built, or the price tag associated with developing a home grown data warehouse over time by programmers or other IT professionals can be overwhelming. Pricing Data Warehouses and OLAP projects could warrant its own blog article.


An OLAP Cube basically takes a spreadsheet and three-dimensionalizes the experiences of analysis. Breaking it down, OLAP means analytical data as opposed to transactional, and the cube part of the nomenclature refers to the storage aspect. OLAP cubes are basically multi-dimensional databases. They store data for analysis, and a lot of BI products rely on OLAP cubes for access to company information for reports, budgets, or dashboards. For example, a CFO might want to report on company financial data by location, by month, or by product – these elements would make up the dimensions of this cube. However, OLAP cubes are not SQL server relational databases, like data warehouses are.

OLAP cubes are not an open SQL server data warehouse, so they require someone with the know-how and the experience to maintain it, whereas a SQL server data warehouse can be maintained by most IT people that have regular database training. This aspect accordingly has a price tag attached to it. Whether allocating time and energy from a current employee to focus on management of the OLAP cube or seeking a new, perhaps full-time employee to join payroll for this role. Additionally, OLAP cubes tend to be more rigid and limited when it comes to designing reports because of their table-like functionality. Aesthetics and capabilities could and arguably should be important to a company who is building their portfolio of BI solutions.



- JiaQii
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156050E

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What is the difference between Data Warehouse and OLAP cube?

A data warehouse serves as a repository to store historical data that can be used for analysis. OLAP is Online Analytical processing that can be used to analyze and evaluate data in a warehouse. The warehouse has data coming from varied sources. OLAP tool helps to organize data in the warehouse using multidimensional models.

Data Warehouse

Data from different data sources is stored in a relational database for end use analysis. Data organization is in the form of summarized, aggregated, non volatile and subject oriented patterns. Supports the analysis of data but does not support data of online analysis.

Online Analytical Processing

With the usage of analytical queries, data is analyzed and evaluated in the data ware house. Data aggregation and summarization is utilized to organize data using multidimensional models. Speed and flexibility for online data analysis is supported for data analyst in real time environment.



- 156050E
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HuiLing

1

OLAP (online analytical processing and cube) refers to a multi-dimensional spreadsheet of data, so an OLAP cube is a staging platform for data analytics. Basically, a cube is a mechanism used to pull together data in organized, dimensional structures for analysis.

Alternately, a data warehouse is a database that stores information to empower decision-making, maintained separately from an organization’s operational database. It supports the handling of organizational data by offering an established platform of consolidated, transactional, organized information. It is a database used for reporting and data analysis (also known as business intelligence).



- HuiLing
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AlvinYap

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A data warehouse is a federated repository for all the data that an enterprise's various business systems collect. The repository may be physical or logical.

A data warehouse is :

-subject-oriented

-integrated

-timevarying

-non-volatile

Data warehousing emphasizes the capture of data from diverse sources for useful analysis and access, but does not generally start from the point-of-view of the end user who may need access to specialized, sometimes local databases. The latter idea is known as the data mart.

There are two approaches to data warehousing, top down and bottom up. The top down approach spins off data marts for specific groups of users after the complete data warehouse has been created. The bottom up approach builds the data marts first and then combines them into a single, all-encompassing data warehouse.

How is a data warehouse different from a regular database?

Data warehouses use a different design from standard operational databases. The latter are optimized to maintain strict accuracy of data in the moment by rapidly updating real-time data. Data warehouses, by contrast, are designed to give a long-range view of data over time. They trade off transaction volume and instead specialize in data aggregation.

Data Warehouse Architecture enter image description here


An OLAP cube is a data structure that allows fast analysis of data. The arrangement of data into cubes overcomes a limitation of relational databases. Relational databases are not well suited for near instantaneous analysis and display of large amounts of data. Instead, they are better suited for creating records from a series of transactions known as OLTP or On-Line Transaction Processing. Although many report-writing tools exist for relational databases, these are slow when the whole database must be summarized.

OLAP cubes can be thought of as extensions to the two-dimensional array of a spreadsheet. For example a company might wish to analyze some financial data by product, by time-period, by city, by type of revenue and cost, and by comparing actual data with a budget. These additional methods of analyzing the data are known as dimensions.Because there can be more than three dimensions in an OLAP system the term hypercube is sometimes used.

The OLAP cube consists of numeric facts called measures which are categorized by dimensions. The cube metadata is typicallyTemplate:Fact created from a star schema or snowflake schema of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.



- AlvinYap
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SandyTang

0

enter image description here



- SandyTang
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tyt2315

0

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Learn more about the BI360 Business Intelligence solution. Acumatica Adaptive Insights anaplan AXUG BI BI360 biznet budgeting business intelligence Cloud cognos collaboration consolidations Dashboards data warehouse DeFacto dynamics ax dynamics gp dynamics nav dynamics sl excel financial reporting forecasting FRx GPUG Host Analytics hyperion Intacct jet reports Management Reporter Microsoft Dynamics Modeling NAVUG OLAP Power BI PowerBI PrecisionPoint prophix QlikView Sage SAP B1 SAP Business One Solver TM1 zap Data Warehouse vs. OLAP Cube Posted on April 28, 2014 by Matthew Felzke

How to store your data is an important facet of Business Intelligence analytics. This article will highlight the differences between Data Warehouses and OLAP Cubes.

Data becomes an increasingly buzz-y trending topic in the business world as the amount of data that a company logs, stores, analyzes, and utilizes continues to grow. Storing and accessing relevant data is imperative for reporting company performance and planning for growth and development in the future. However, outside of having an IT or programming background, comprehending the technology options for data storage can be a challenge. The options are few, but their functionalities vary, and depending on the various Business Intelligence (BI) requirements that need to be met, the product may require a certain data storage solution. This article is going to focus on discussing and comparing the two most common options: Data Warehouse versus an OLAP Cube.

First things first: defining the two options. A data warehouse is simply a database that houses information to support decision-making, managed separately from a company’s operational database. It supports the processing of organizational information by offering a stable platform of consolidated, transactional, organized data. On the other hand, OLAP stands for online analytical processing and cube is another word for a multi-dimensional set of data, so an OLAP cube is a staging space for analysis of information. Basically, a cube is a mechanism used to query data in organized, dimensional structures for analysis. These two options have different IT requirements.

Data Warehouses historically have been a development project that can be pretty pricey just to build. However, data warehouses are now also being offered as a product – fully built, configurable, and able to house multiple types of data. Some data warehouse solutions can be managed by the business user. An OLAP cube is not an open SQL server data warehouse, so it requires someone with OLAP technical skills and experience to manage the server. This translates to specific personnel requirements, but as OLAP cubes are being used all over the business sector, there is logically enough people in the workforce with the skill set required to manage the cube – if the budget allows for the expense of this position. While cost is an important factor to consider, there are several characteristics to think about.

Understanding data warehouses and OLAP cubes better is extremely beneficial for making choices about implementation of BI tools. For some, since company data is needed for such regular analysis, data storage could very well drive the route an organization would take for acquiring BI solutions. For others, the investment in a vault of transactional data might be a secondary purchase, in response to BI processing needs. Either way, the differences are important when making a data storage decision.

A Data Warehouse is organized with business user accessibility at the center of design. It is subject-structured, meaning that it is organized around topics like product, sales, and customer. Because data must be replicated from an Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or any other data organization system, data warehouses are not used for live analysis. Instead, the information housed within data warehouses can be used for periodical reporting; planning, forecasting, and modeling; and in dashboards or graphical scorecards where trends and trajectories of company data can be visually analyzed. Moreover, a warehouse can house a wide variety of data types.

A data warehouse is crafted in such a way that it can integrate several, disparate data sources to create a consolidated database. This is done through data cleaning and data integration techniques that are “smart” processes innate to the data warehouse. Therefore, a company can store personnel data, financial transactions, and any other organizational information all in one place – going beyond numbers and cash flow. It is a very accessible storage unit where data is replicated and transformed from the operational environment. There are really only two operations when accessing the data: the initial loading of the information and the access itself. Data warehouses do not require any recovery, transaction processing or concurrency control mechanisms – they stand simply and securely on their own. Data warehouses are so efficient in terms of business user accessibility because of their SQL server framework. However, they can be bought directly already built, or the price tag associated with developing a home grown data warehouse over time by programmers or other IT professionals can be overwhelming. Pricing Data Warehouses and OLAP projects could warrant its own blog article.

An OLAP Cube basically takes a spreadsheet and three-dimensionalizes the experiences of analysis. Breaking it down, OLAP means analytical data as opposed to transactional, and the cube part of the nomenclature refers to the storage aspect. OLAP cubes are basically multi-dimensional databases. They store data for analysis, and a lot of BI products rely on OLAP cubes for access to company information for reports, budgets, or dashboards. For example, a CFO might want to report on company financial data by location, by month, or by product – these elements would make up the dimensions of this cube. However, OLAP cubes are not SQL server relational databases, like data warehouses are.

OLAP cubes are not an open SQL server data warehouse, so they require someone with the know-how and the experience to maintain it, whereas a SQL server data warehouse can be maintained by most IT people that have regular database training. This aspect accordingly has a price tag attached to it. Whether allocating time and energy from a current employee to focus on management of the OLAP cube or seeking a new, perhaps full-time employee to join payroll for this role. Additionally, OLAP cubes tend to be more rigid and limited when it comes to designing reports because of their table-like functionality. Aesthetics and capabilities could and arguably should be important to a company who is building their portfolio of BI solutions.

The take-home of this article could be that, in looking for BI tools, consider what format of data storage the product utilizes for analysis. Knowing and understanding the pros and cons of data warehouses and OLAP cubes should result in a preference – and the storage of company data, exponentially bigger amounts by the year, is arguably a foundation for successful analysis. Solver offers a fully built, configurable data warehouse stand-alone and as part of the comprehensive suite of BI modules and would be happy to answer questions and generally review BI360’s easy-to-use Data Warehouse solution for collaborative, streamlined decision-making capabilities. This entry was posted in Business Intelligence, Solver Operational Data Store (BI360 Data Warehouse module) and tagged Acumatica, data, data storage, data warehouse, dynamics ax, dynamics gp, dynamics nav, dynamics sl, Intacct, Microsoft, Netsuite, OLAP, OLAP Cubes, Sage 500, sage x3, Salesforce, SAP B1, SAP Business One, SQL Server by Matthew Felzke. Bookmark the permalink. Matthew Felzke About Matthew Felzke I am a Communications and Event Marketing Manager based in Los Angeles, CA. I enjoy building community and being inquisitive and resourceful in order to connect people. I have my Master's degree in writing, and I am focusing my energies online these days. In my free time, I run, hike, explore urban spaces, and look for my new favorite food experience.



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